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Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level emotional dictionary for social emotion detection. In the dictionary, each word is associated with the(More)
The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social media materials. Compared to the classical sentiment analysis from writers' perspective, sentiment analysis(More)
The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's(More)
Sentiment analysis of online documents such as news articles , blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three(More)
With the development of the Internet, user-generated data has been growing tremendously in Web 2.0 era. Facing such a big volume of resources in folksonomy, people need a method of fast exploration and indexing to find their demanded data. To achieve this goal, contextual information is indispensable and valuable to understand user preference and purpose.(More)
Expert finding is important to the development of community question answering websites and e-learning. In this study, we propose a topic-sensitive probabilistic model to estimate the user authority ranking for each question, which is based on the link analysis technique and topical similarities between users and questions. Most of the existing approaches(More)
Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled(More)
Several existing recommender algorithms combine collaborative filtering and social/trust networks together in order to overcome the problems caused by data scarcity and to produce more effective recommendations for users. In general, those methods fuse a user’s own taste and his trusted friends/users’ tastes using an ensemble model where a parameter is used(More)